IEEE Transactions on Reliability最新文献_第2页

A Novel Adaptive System-Level Fault Self-Diagnosis Algorithm and Its Applications 一种新的自适应系统级故障自诊断算法及其应用

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2025-04-09 DOI: 10.1109/TR.2025.3553903

Fuxing Liao;Jiafei Liu;Chia-Wei Lee;Sun-Yuan Hsieh;Jingli Wu

{"title":"A Novel Adaptive System-Level Fault Self-Diagnosis Algorithm and Its Applications","authors":"Fuxing Liao;Jiafei Liu;Chia-Wei Lee;Sun-Yuan Hsieh;Jingli Wu","doi":"10.1109/TR.2025.3553903","DOIUrl":"https://doi.org/10.1109/TR.2025.3553903","url":null,"abstract":"With the application and rapid development of high-performance computing and cloud computing technology, the scale of the interconnection network has appeared to grow exponentially. Network attacks have become increasingly sophisticated and stealthy. To reach a high reliable network system, widespread attention has been paid to fault diagnosis. In this article, we put forward a reliable and adaptive self-diagnosis strategy, the <inline-formula><tex-math>$h$</tex-math></inline-formula>-extra <inline-formula><tex-math>$r$</tex-math></inline-formula>-component conditional diagnosability, denoted by <inline-formula><tex-math>$ct_{r}^{h}(G)$</tex-math></inline-formula>. Then, we provide a theoretical derivation to characterize the <inline-formula><tex-math>$h$</tex-math></inline-formula>-extra <inline-formula><tex-math>$r$</tex-math></inline-formula>-component conditional diagnosability of bubble sort networks <inline-formula><tex-math>$B_{n}$</tex-math></inline-formula> under the PMC model. Furthermore, we develop a fast and adaptive fault self-diagnosis algorithm FAFD-PMC to detect all faulty units. Extensive experiments are implemented and applied to synthetic networks and real networks in terms of accuracy (ACCR), true negative rate, false positive rate, recall, and precision, which demonstrates the ACCR/efficiency of our algorithm.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"4294-4305"},"PeriodicalIF":5.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Insights From Bugs in FPGA High-Level Synthesis Tools: An Empirical Study of Bambu Bugs FPGA高级合成工具中bug的见解：Bambu bug的实证研究

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2025-04-08 DOI: 10.1109/TR.2025.3547739

Zun Wang;He Jiang;Xiaochen Li;Shikai Guo;Xu Zhao;Yi Zhang

{"title":"Insights From Bugs in FPGA High-Level Synthesis Tools: An Empirical Study of Bambu Bugs","authors":"Zun Wang;He Jiang;Xiaochen Li;Shikai Guo;Xu Zhao;Yi Zhang","doi":"10.1109/TR.2025.3547739","DOIUrl":"https://doi.org/10.1109/TR.2025.3547739","url":null,"abstract":"High-level synthesis (HLS) tools have been widely used in field-programmable gate array (FPGA) design to convert C/C++ code to hardware description language code. Unfortunately, HLS tools are susceptible to bugs, which can introduce serious vulnerabilities in FPGA products, leading to substantial losses. However, the characteristics of these bugs (e.g., root causes and bug-prone stages) have never been systematically studied, which significantly hinders developers from effectively handling HLS tool bugs. To this end, we conduct the first empirical study to uncover HLS tool bug characteristics. We collect 349 bugs of a widely used HLS tool, namely Bambu. We study the root causes, buggy stages, and bug fixes of these bugs by applying a multiperson collaboration method. Finally, 13 valuable findings are summarized. We find 14 categories of root causes in Bambu bugs; most bugs (22.1%) are caused by incorrect implementation of IR processing; the front end of Bambu is more bug-prone; to fix these bugs, 2.27 files and 80.19 lines of code need to be modified on average. We also present the insights gained from 95 Vitis HLS bugs. From these findings, we suggest that developers could use an on-the-fly code generator configuration method to generate suitable testing programs for HLS tool bug detection and apply large language models to assist in fixing HLS tool bugs.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3341-3355"},"PeriodicalIF":5.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stealthy Query-Efficient OpaqueAttack Against Interpretable Deep Learning 针对可解释深度学习的隐形查询高效不透明攻击

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2025-04-02 DOI: 10.1109/TR.2025.3551717

Eldor Abdukhamidov;Mohammed Abuhamad;Simon S. Woo;Eric Chan-Tin;Tamer Abuhmed

{"title":"Stealthy Query-Efficient OpaqueAttack Against Interpretable Deep Learning","authors":"Eldor Abdukhamidov;Mohammed Abuhamad;Simon S. Woo;Eric Chan-Tin;Tamer Abuhmed","doi":"10.1109/TR.2025.3551717","DOIUrl":"https://doi.org/10.1109/TR.2025.3551717","url":null,"abstract":"Deep neural network (DNN) models are susceptible to adversarial samples in white-box and opaqueenvironments. Although previous studies have shown high attack success rates, coupling DNN models with interpretation models could offer a sense of security when a human expert is involved. However, in white-box environments, interpretable deep learning systems (IDLSes) have been shown to be vulnerable to malicious manipulations. As access to the components of IDLSes is limited in opaquesettings, it becomes more challenging for the adversary to fool the system. In this work, we propose a <italic>Query-efficient <italic>Score-based opaque attack against IDLSes, which requires no knowledge of the target model and its coupled interpretation model. By continuously refining the adversarial samples created based on feedback scores from the IDLS, our approach effectively reduces the number of model queries and navigates the search space to identify perturbations that can fool the system. We evaluate the attack's effectiveness on four convolutional neural network (CNN) models and two interpretation models, using both ImageNet and CIFAR datasets. Our results show that the proposed approach is query-efficient with a high attack success rate that can reach more than 95%, and an average transferability success rate of 69%. We have also demonstrated that our attack is resilient against various preprocessing defense techniques.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3484-3498"},"PeriodicalIF":5.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Physical-Statistical Framework on Complex Mechanical System Fault Isolation 复杂机械系统故障隔离的物理统计框架

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2025-04-01 DOI: 10.1109/TR.2025.3549216

Bingxin Yan;Qiuzhuang Sun;Lijuan Shen;Xiaobing Ma

{"title":"A Physical-Statistical Framework on Complex Mechanical System Fault Isolation","authors":"Bingxin Yan;Qiuzhuang Sun;Lijuan Shen;Xiaobing Ma","doi":"10.1109/TR.2025.3549216","DOIUrl":"https://doi.org/10.1109/TR.2025.3549216","url":null,"abstract":"Supervisory control and data acquisition (SCADA) data from a complex mechanical system, such as a high-speed train power bogie, nonpower bogie, and wind turbine, are widely used for anomaly detection and fault isolation. The SCADA data include measurements of process variables and exogenous covariates for key components in the system. The process variables refer to the performance characteristics of the key component while the exogenous covariates are working loads or working conditions of the complex mechanical system. Dominated by such physical mechanisms as dynamic motion laws of the system, there are complex relationships between the process variables and covariates, that complicate anomaly detection and fault isolation. To solve this problem, we propose a framework that integrates physical knowledge and statistical learning. We first build a spline model to capture the relationship between process variables and exogenous covariates. To make the model interpretable, we use physical knowledge to impose constraints on the model parameters. We then conduct anomaly detection at a system level based on the physical-statistical regression model. Once an anomaly is detected, we propose a Lasso-based method to isolate the faulty components. Our fault isolation method does not require historical failure data or knowing the true number of faulty components. Real-world case studies on power bogies from high-speed trains illustrate the advantages of our framework: the best benchmark achieves at least 2.50% lower F1-score in anomaly detection and 6.01% lower F1-score in fault isolation compared to our method.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"4091-4105"},"PeriodicalIF":5.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resilience Assessment for Hybrid AC/DC Cyber-Physical Power Systems Under Cascading Failures 级联故障下交直流网络物理混合电力系统的恢复能力评估

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2025-04-01 DOI: 10.1109/TR.2025.3550523

Kaishun Xiahou;Wei Du;Xingye Xu;Zhenjia Lin;Yang Liu;Zhaoxi Liu;Qiuwei Wu

引用次数: 0

Extracting Meaningful Issue–Solution Pair From Collaborative Developer Live Chats 从协作开发人员实时聊天中提取有意义的问题-解决方案对

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2025-04-01 DOI: 10.1109/TR.2025.3550412

Jiawen Shen;Shikai Guo;Longfeng Chen;Chen Wu;Hui Li;Chenchen Li

{"title":"Extracting Meaningful Issue–Solution Pair From Collaborative Developer Live Chats","authors":"Jiawen Shen;Shikai Guo;Longfeng Chen;Chen Wu;Hui Li;Chenchen Li","doi":"10.1109/TR.2025.3550412","DOIUrl":"https://doi.org/10.1109/TR.2025.3550412","url":null,"abstract":"The live chats of developers often contain meaningful information in the form of issue–solution pairs. The issue–solution pairs can offer helpful references to others who seek solutions for the similar issues, which can improve software development efficiency by facilitating issue solving. However, previous approaches such as ISPY still struggle with unsatisfactory extraction accuracy, due to the entanglement and complexity of issue-solution pairs' feature information. To address these challenges, we propose an approach named <italic>IS-Hunter for mining issue-solution pairs from real-time chat data. Specifically, <italic>IS-Hunter consists of four main components: the data preprocessing component disentangles and denoises raw chat logs, the utterance embedding component embeds utterances into vectors that subsequent components can easily process, the feature extraction component obtains textual, heuristic, and contextual feature that determines whether an utterance is topic-relevant, and the issue–solution pair prediction component predicts the utterance whether is an issue or a solution. The experimental results show that the performance of IS-Hunter outperforms the baseline methods in issue-detection and solution-extraction in terms of Precision, Recall, and F1-score. Compared with baseline methods, in issue-detection, IS-Hunter, respectively, achieves an average precision, recall, and F1-score of 0.74, 0.74, and 0.74, and it marks an obvious 4.23% improvement over the state-of-the-art approaches. Simultaneously, in solution-extraction, IS-Hunter achieves an average precision, recall, and F1-score of 0.83, 0.90, and 0.86 which is 4.88% higher than the best baseline methods.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3600-3614"},"PeriodicalIF":5.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FNS-CATF-CAC: An Efficient Crosstalk Avoidance Code to Reduce the Switching Activity in TSV Arrays FNS-CATF-CAC：一种有效的串扰避免码，以减少TSV阵列中的切换活动

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2025-03-31 DOI: 10.1109/TR.2025.3550972

Chen Wei;Xiaole Cui

引用次数: 0

Information Correction–Based Analytical Model for Fault Section Diagnosis of Power Systems 基于信息校正的电力系统故障路段诊断分析模型

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2025-03-27 DOI: 10.1109/TR.2025.3549059

Guojiang Xiong;Shunshun Sun

引用次数: 0

Unsupervised Software Defect Prediction Through Multiview Clustering 基于多视图聚类的无监督软件缺陷预测

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2025-03-26 DOI: 10.1109/TR.2025.3548107

Zhiqiang Li;Hongyu Zhang;Xiao-Yuan Jing;Wangyang Yu;Yueyue Liu

{"title":"Unsupervised Software Defect Prediction Through Multiview Clustering","authors":"Zhiqiang Li;Hongyu Zhang;Xiao-Yuan Jing;Wangyang Yu;Yueyue Liu","doi":"10.1109/TR.2025.3548107","DOIUrl":"https://doi.org/10.1109/TR.2025.3548107","url":null,"abstract":"The core goal of software defect prediction (SDP) is to identify modules with a high likelihood of defects, thereby enabling prioritization of quality assurance activities with low inspection effort. There are many supervised defect prediction models that are extensively studied. However, these methods require the need for labeling data to get enough training modules, which will cause a lot of waste of human resources. Cross-project defect prediction primarily reuses models trained on other projects with enough historical data. However, this strategy is often hindered by large distribution differences across different projects and privacy concerns of data. Unsupervised learning technique is an alternative solution to the unlabeled data, but it mainly focuses on single-view prediction by concatenating all the software metrics. This ignores the diversity and complementarity of different types of metrics. This study proposes a novel approach, namely, multiview unsupervised software defect prediction (MUSDP). It aims to collaboratively learn the diversity and complementarity of different views to build a robust and reliable defect prediction model. Extensive experiments on <inline-formula><tex-math>$ 28$</tex-math></inline-formula> releases from eight software projects indicate that MUSDP exhibits superior or comparable results regarding <italic>G-mean, <italic>AUC, <inline-formula><tex-math>$P_{text{opt}}$</tex-math></inline-formula>, and <italic>Recall@20% compared to competing supervised and unsupervised methods. For the interpretation of MUSDP, the number of added and deleted lines significantly influence its predictions.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3356-3370"},"PeriodicalIF":5.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating Mean Time to Failure of Solid-State Drives via Self-Organizing Map and Model Averaging 利用自组织映射和模型平均估计固态硬盘的平均故障时间

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2025-03-26 DOI: 10.1109/TR.2025.3550380

Peng Li;Xun Xiao;Jiayu Chen

{"title":"Estimating Mean Time to Failure of Solid-State Drives via Self-Organizing Map and Model Averaging","authors":"Peng Li;Xun Xiao;Jiayu Chen","doi":"10.1109/TR.2025.3550380","DOIUrl":"https://doi.org/10.1109/TR.2025.3550380","url":null,"abstract":"In this article, a two-step approach is developed to estimate mean time to failure (MTTF) of solid-state drives (SSD) by first formulating a composite health indicator via multichannel signal fusion and further predicting the remaining useful life(RUL) under degradation model misspecification. Specifically, an unsupervised neural network based on self-organizing map is constructed to approximate the highly nonlinear relationship between multivariate monitoring attributes and a univariate SSD health indicator. For each SSD, the composite health indicator over time is further calibrated by smoothing techniques and formulated into a general path degradation model with a uniform failure threshold. By extrapolating each degradation path to hit the failure threshold, the RULs of SSDs are obtained as pseudofailure times, which are fitted by various lifetime distributions. Finally, a novel model averaging strategy is proposed to weigh the MTTFs estimated by multiple combinations of candidate degradation models and lifetime distributions to alleviate the impact of model misspecification. A real-world SSD dataset is used to demonstrate the feasibility of the proposed two-step approach. Numerical results suggest that the proposed approach better characterizes the underlying degradation process under different model assumptions and settings.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"4417-4425"},"PeriodicalIF":5.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0